1 / 23

Enhancing MIMO Broadcast Channels with Dirty Paper Coding: Applications in Watermarking

Explore the application of Dirty Paper Coding (DPC) in MIMO broadcast channels, specifically in watermarking scenarios. Learn about coding in Gaussian cases, achieving capacity, practical comparisons, outer and inner encoder structures, performance criteria, and more.

celiat
Download Presentation

Enhancing MIMO Broadcast Channels with Dirty Paper Coding: Applications in Watermarking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Codage avec Information Adjacante (DPC : Dirty paper coding) et certaines de ses applications : Tatouage (Watermarking)MIMO broadcast channels Gholam-Reza MOHAMMAD-KHANI

  2. Channel capacity (Gel’fand and Pinsker 1980) Gel’fand and Pinsker’s channel • Channel definition Encoder

  3. Channel description (Dirty paper coding - Costa 1983) • Coding Gaussian case (DPC)

  4. Channel description (Dirty paper coding - Costa 1983) Gaussian case (DPC) • Coding S   W U X Encoder

  5. Channel description (Dirty paper coding - Costa 1983) DPC Application for Watermarking • Watermarking Application : • X : Mark (Weak Signal) , S : Host (Strong Signal) , Z : Noise • Capacity Achieving for Mark Signal

  6. r1 antennas Y1 : Decoder #1 : W1 YK : X Decoder #K Encoder WK t antennas H rK antennas p(y|x,H) Problem statement in MIMO BC H1 HK

  7. Performance Criteria in BC : • Usual Criteria (Information Theory Aspects) : • Capacity Regions • Throughput (Sum Capacity) • New Criteria (Practical Aspects) : • BER Regions • Number of Satisfied Users (of Rates or of BER)

  8. Some Relateds Works : • Sato : • Upperbound for Sum Capacity of BC • - Cover [72] : • Definition of Broadcast Channels • - Weingarten & Shamai [06] : • Capacity Region of Gaussian MIMO BC • - Caire & Shamai [03] + Viswanath & Tse [03] + • Vishwanath & Goldsmith [03] + Yu & Cioffi [04]: • Achievable Throughput of Gaussian MIMO BC • DPC scheme : • Achieve Sum Capacity and Capacity Region for MIMO BC

  9. r1 antennas Y1 Decoder #1 : YK Decoder #K rK antennas DPC and MIMO BC : H1 W1 : X Encoder WK HK t antennas H p(y|x,H)

  10. Channel model and capacity region Superposition coding: One Simple Case : Gaussian SISO BC

  11. DPC vs TDMA • Theorique Comparison : • Jindal & Goldsmith [05] : • Best performance of DPC on Sum Capacity • Weingarten & Shamai [06] : • Best Performance of DPC on Capacity Region • Practical Comparison : • Belfiore [06] • Mohammad-Khani & Lasaulce [06] • Sensibility to Channel Estimation • BER Comparison

  12. Outer Encoder Inner Encoder Structure of DPC schemes for Gaussian MIMO BCs • Encoder structure W1 : X WK H • Outer encoders : Linear • Pre-equalizers: MF, ZF, MMSE • ZF-DPC • MMSE-DPC • Outer encoders • Tomlinson Harashima precoder (THP) • Scalar Costa’s scheme (SCS) • Trellis coded quantization (TCQ) + turbo • Nested lattices

  13. Structure of DPC schemes for Gaussian MIMO BCs • Encoder structure

  14. Comparison of outer coders

  15. Received signal structure Inner coding • Possible approaches • Linear precoding with successive coding using DPC as outer coding (the outer coder treats the interference) • Linear pre-equalizer with independent outer coder (the outer coder does not treat the interference) • Comments • Inner coding  space-time coding or beamforming • Inner + outer coding  implements a good multiple access scheme

  16. MMSE-DPC • Main features • Optimum in the sense of the sum-capacity • Two ways of implementing it: • Yu & Cioffi 04 (GDFE precoder) • Viswanath & Tse 03 (duality BC – MAC) • Precoding filters depend on power allocation • Coding order: no effect on sum capacity (not true for the capacity region) • Power allocation: we used the policy proposed by Boche & Jorswieck 04 (corresponding numerical algorithms converge) Numerical technique

  17. ZF-DPC • Main features • Introduced by Caire & Shamai 03 (for single-antenna receivers) • We generalized this scheme to multi-antenna receivers • Simpler than MMSE-DPC but suboptimum in terms of sum-capacity • Quasi-optimal in terms of sum-capacity, when H is full row rank • Number of served users limited to rank of H • Sensitive to coding order Waterfilling :

  18. Influence of the coding order: example • Conclusions • Coding order has no effect on sum rate for MMSE-DPC • Sum rate of ZF-DPC strongly depends on coding order • Coding order can be optimized by a greedy algorithm [Tu & Blum03] • If the coding order is not well chosen: TDMA can perform better than DPC (especially for low SNRs)

  19. Conventional pre-equalizers • Definitions • ZF : • MMSE : • MF : Water-Filling Numerical Method to compute Sum Rate • Comments • The outer coder does not help to the interference cancellation task (separate coding) • No successive coding = no coding order • Most simple schemes when the CSI is known

  20. Comparison of inner coders (1/2) Sum Rate Comparison

  21. Comparison of inner coders (2/2) Region of achieved Rate Comparison P=10dB P=7dB P=20dB

  22. Overall performance (1/2) Degraded channel (No need to inner coder) Application de TCQ pour un BC scalaire dégradé 2 utilisateurs 0 z2 y2 u2 x2 Viterbi Decoder TCQ x z1 u1 x1 Viterbi Decoder TCQ y1

  23. Overall performance (2/2)

More Related